For an upcoming Drift Station project, we’ve been considering how to curatorially sort a massive number of images (about 100k) for presentation. Chronological? Random order? Some other logical scheme? But a more computational approach seemed to make sense: some way of parsing the images that took into account a variety of visual factors in each image, something that would be impossible to do manually.
Neural networks are the obvious answer here, and so I found some very helpful sample code from Gene Kogan and Kyle McDonald, and wrote some Python and Processing code that loads up a folder of images and extracts a vector representation from them. Then, using t-SNE and Rasterfairy, the images were organized into a 2D grid.
I’ve spent the last few days playing with settings in the code, and found there is an interesting balance to be struck between locally preserving color similarity and object similarity. (Note: this post is more of a quick note than a deep-dive analysis.)
Above: a version with blurred images, showing a pretty clear separation by color with fairly smooth transitions. Click on images for a higher-res version. Continue reading “Arranging By Color And Objects With t-SNE”